Optimization calculation method, system and medium for following vehicle distance in unmanned vehicle platooning

Abstract

The present invention discloses a method, system and medium for optimizing the following distance calculation of unmanned vehicle platooning. The method comprises the following steps: analyzing the quantitative variation characteristics of the air drag coefficient and lift coefficient of vehicles in the unmanned vehicle platoon; calculating the minimum following distance of the following vehicle based on the quantitative variation characteristics, a nonlinear dynamic model and the prediction iteration of the vehicle time-varying state data; determining the following distance optimization interval according to the minimum following distance, analyzing the influencing parameters between the following distance and the braking margin, energy consumption, lift coefficient and the tracking ability of the leading vehicle; dynamically calculating the optimal following distance of the following vehicle according to the influencing parameters within the following distance optimization interval; the present invention can obtain the quantitative law of the air drag coefficient and lift coefficient of the platoon vehicles, comprehensively consider the vehicle speed, road resistance, transmission efficiency and the response characteristics of the driving / braking elements, and dynamically predict and iteratively calculate the optimal following distance of each following vehicle in real time.

Description

Technical Field

[0001] The present invention relates to the field of vehicle spacing control technology. Specifically, the present invention is applied to the field of joint control of unmanned vehicle subsystems, and in particular to a method, system and medium for optimizing the following vehicle spacing when unmanned vehicles are traveling in formation. Background Art

[0002] Unmanned vehicle platooning technology is seen as a key part of future intelligent transportation systems, with significant application value and market potential. This technology effectively reduces vehicle distances by precisely controlling platooning between vehicles, and utilizes the wind-breaking effect of the leading vehicle to reduce wind resistance and fuel consumption of the following vehicle.

[0003] During platooning, the following distance has a significant impact on energy consumption, so optimizing the following control technology and reasonably setting and adjusting the following distance are crucial. Current research mainly focuses on the overall stability and fuel economy of the platoon, and calculates the control instructions of the optimal drive components through dynamic models, but usually only considers local optimal solutions, and research on the optimal following distance is relatively insufficient. Commonly used following distance strategies include fixed distance, fixed time distance, and variable time distance strategies, but nonlinear time-varying factors such as vehicle model differences, vehicle sequence, drag coefficient, lift coefficient, drive efficiency, braking characteristics, and road slope make the determination of the optimal following distance complicated. Previously, no one has proposed a quantitative prediction method for the optimal following distance based on iterative calculation of dynamic models that comprehensively considers all nonlinear time-varying factors.

[0004] For example, Chinese patent CN108284836A discloses a vehicle longitudinal following control method. This patent proposes a vehicle platooning control scheme based on nonlinear model predictive control theory, aiming to improve fuel economy by controlling the speed and expected vehicle-to-vehicle distance in the queue by optimizing the control variable. However, the key expected vehicle-to-vehicle distance is directly set without quantitative optimization calculations, and factors such as the dynamic changes in driving resistance and the changes in the lift coefficient of the following vehicle caused by the leading vehicle are not considered.

[0005] For example, Chinese patent CN116749969A discloses a method for controlling the following distance of a vehicle platoon based on driving conditions and road surface conditions. This method can change the following strategy according to different road conditions to ensure following safety and platoon stability. However, this method does not consider the nonlinear dynamic characteristics of the vehicle to calculate the minimum following distance, nor does it deeply explore how to adjust the following distance based on changes in the drag coefficient to optimize energy consumption.

[0006] For example, Chinese patent application CN110164124A discloses a method for controlling the longitudinal following of vehicles in a platoon of heavy trucks on highways. This method considers the impact of uncertain factors such as road slope, transmission delay, wind speed, and the acceleration of the preceding vehicle on vehicle speed and spacing control. However, this method also fails to consider the key factor of optimizing the desired following distance.

[0007] As the literature <Optimizing Gap Tracking Subject to Dynamic Losses viaConnected and Anticipative MPC in Truck Platooning> This paper considers the drag coefficient that varies with distance and establishes a dynamic model to optimize vehicle speed and spacing control; however, its target following distance is still given directly and no specific calculation method is given.

[0008] For example, Chinese patent CN117850406A discloses an energy-saving scheduling method and system based on autonomous driving truck platoons. This method calculates the minimum instantaneous fuel consumption rate by constructing a fuel consumption module. However, this method only considers the speed of a single vehicle in the current sub-section, and does not consider the impact of wind resistance caused by changes in the following vehicle distance, the interaction between vehicles, and the overall effect of platooning. Therefore, the optimal solution obtained is local.

[0009] For example, Chinese patent CN117270525A discloses a method and device for controlling an unmanned truck formation based on energy consumption optimization. This solution calculates the optimal expected speed by establishing a dynamic model of the unmanned truck formation, but does not consider the relationship between the optimal speed and the optimal spacing, nor does it explore how to determine the following distance based on energy consumption factors.

[0010] For example, Chinese patent CN114818109A discloses a method for calculating the air resistance coefficient and fuel economy of vehicles in a platoon. This method uses CFD to calculate the air resistance coefficients of individual vehicles and platoon vehicles, and then calculates the fuel savings of the platoon. However, this method only calculates fuel consumption at different spacings and does not address the issue of how to dynamically predict and control the optimal spacing in real time based on road conditions.

[0011] For example, Chinese patent CN116161031A discloses a fuel-saving autonomous truck following control method and device. The method introduces an automatic vehicle following system that calculates and adjusts the following distance through a distance measuring device to achieve energy saving. However, when discussing the minimum following distance, the source of the braking deceleration value and the sampling error are not considered, nor is the influence of the road slope. It is also unclear how to quantitatively convert air resistance analysis into following distance calculation. In actual applications, the optimization of following distance requires comprehensive consideration of multiple factors and cannot simply pursue minimization.

[0012] In addition, in existing research on following distance calculation, traditional methods usually simulate the braking execution delay of the following vehicle by simply adding a preset delay after the braking command is issued; however, this method is not applicable to new energy electric vehicles because the electric braking system of electric vehicles responds quickly and can quickly apply brakes without waiting for the mechanical braking system to build up braking pressure, thereby significantly reducing the braking delay under certain conditions; therefore, when calculating the optimal following distance, the dynamic response capabilities of electric braking and mechanical braking should be considered separately, and quantitative calculations of nonlinear characteristics should be performed to ensure accuracy and effectiveness in practical applications. Summary of the Invention

[0013] The purpose of the present invention is to provide a method, system and medium for optimizing the following distance calculation of unmanned vehicle platooning, thereby solving all or one of the above-mentioned problems existing in the prior art.

[0014] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0015] In one aspect, the present invention provides a method for optimizing the following distance between vehicles in an unmanned vehicle platoon, comprising the following steps:

[0016] Analyze the quantitative variation characteristics of the air drag coefficient and lift coefficient of vehicles in an unmanned vehicle fleet;

[0017] Establishing a nonlinear dynamic model of the following vehicle, and calculating a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model, and the prediction iteration of the vehicle time-varying state data;

[0018] An optimization interval for following distance is determined based on the minimum following distance, and the influencing parameters between following distance and braking margin, energy consumption, lift coefficient, and tracking capability of the leading vehicle are analyzed; an optimal following distance for the following vehicle is dynamically calculated within the optimization interval based on the vehicle longitudinal dynamics model, the iterative relationship between the vehicle time-varying state data, and the influencing parameters; and the following strategy of the unmanned vehicle fleet is controlled based on the optimal following distance.

[0019] Furthermore, the establishing of a nonlinear dynamic model of the following vehicle and the calculation of a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model, and the prediction iteration of the time-varying state data of the vehicle include:

[0020] determining a time iteration relationship during the vehicle following process, and determining an iteration relationship of the time-varying state data of the pilot vehicle when the pilot vehicle fully brakes based on the time iteration relationship;

[0021] Calculating a discrete state space equation of the system based on the nonlinear dynamic model, and determining an iterative relationship of time-varying state data of the following vehicle according to the quantitative change characteristics and the discrete state space equation of the system;

[0022] A dynamic following distance and a minimum distance condition are set, the dynamic following distance is iterated based on the time iteration relationship, the iteration relationship of the time-varying state data of the lead vehicle, the system discrete state space equation, and the iteration relationship of the time-varying state data of the following vehicle, and whether the iterated dynamic following distance is used as the minimum following distance is determined according to the minimum distance condition.

[0023] Furthermore, the discrete state space equation of the computing system based on the nonlinear dynamic model includes:

[0024] constructing a nonlinear MPC model predictive controller for the following vehicle motion, and transforming a vehicle longitudinal dynamics model of the following vehicle according to the nonlinear MPC model predictive controller to obtain a continuous state space equation based on the vehicle longitudinal dynamics model;

[0025] The continuous state space equation is discretized by using forward Euler integration to obtain the discrete state space equation of the system.

[0026] Furthermore, the time-varying state data of the pilot vehicle include: the time-varying driving speed and the time-varying driving distance of the pilot vehicle when fully braking;

[0027] The time-varying state data of the following vehicle includes: the time-varying following distance of the following vehicle, the time-varying air resistance coefficient, the time-varying lift coefficient, the time-varying road resistance coefficient, the time-varying road slope manifold data, and the time-varying traction or braking force.

[0028] Furthermore, the minimum distance condition includes: a first condition, a second condition, a third condition and a fourth condition;

[0029] The first condition is: D d3(K3) d2(K2);

[0030] The second condition is: D=d3(K3) d2(K2);

[0031] The third condition is: D d3(K3) d2(K2);

[0032] The fourth condition is: ;

[0033] in, is the dynamic following distance, is the time-varying following distance of the following vehicle, is the time-varying distance traveled by the pilot vehicle when fully braking, is the first threshold value for the following distance.

[0034] Furthermore, the determining, based on the minimum distance condition, whether the iterative dynamic following distance is used as the minimum following distance includes:

[0035] In response to the dynamic following distance satisfying the first condition, determining that the following vehicle and the lead vehicle have collided, increasing the dynamic following distance, iteratively calculating the time-varying following distance of the following vehicle and the time-varying travel distance of the lead vehicle under full braking based on the increased dynamic following distance, and determining the minimum distance condition;

[0036] In response to the dynamic following distance satisfying the second condition, determining that a critical collision point occurs between the following vehicle and the lead vehicle;

[0037] In response to the dynamic following distance satisfying the third condition, determining that the following vehicle and the lead vehicle do not collide, reducing the dynamic following distance, iteratively calculating the time-varying following distance of the following vehicle and the time-varying travel distance of the lead vehicle under full braking based on the reduced dynamic following distance, and determining the minimum distance condition;

[0038] In response to the dynamic following distance satisfying the fourth condition, it is determined that the dynamic following distance is the minimum following distance.

[0039] Furthermore, dynamically calculating the optimal following distance of the following vehicle within the following distance optimization interval based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influencing parameters includes:

[0040] Determining a final optimal function for the following vehicle distance based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influencing parameters;

[0041] The following distance is dynamically optimized within the following distance optimization interval based on the final optimization function to obtain the optimal following distance.

[0042] Furthermore, the influencing parameters include: braking margin influencing parameters, energy consumption influencing parameters, lift coefficient influencing parameters and tracking capability influencing parameters;

[0043] The braking margin influencing parameter is: the minimum following distance;

[0044] The energy consumption influencing parameter is: an optimal efficiency value following distance determined based on energy consumption data iterated based on the iterative relationship between the vehicle longitudinal dynamics model, the vehicle time-varying state data, and the dynamic following distance;

[0045] The lift coefficient influencing parameter is: a lift coefficient that varies with the dynamic following distance;

[0046] The tracking capability influencing parameter is: a leading vehicle confidence level that varies with the dynamic following distance;

[0047] The final optimization function is: ,in is the braking margin influencing parameter, is the energy consumption influencing parameter, is the lift coefficient influencing parameter, is the tracking capability influencing parameter, is the dynamic following distance.

[0048] On the other hand, the present invention also provides a system for optimizing the following distance between vehicles in an unmanned vehicle platoon, comprising:

[0049] The variation characteristics analysis module is used to analyze the quantitative variation characteristics of the air resistance coefficient and lift coefficient of vehicles in the unmanned vehicle fleet;

[0050] a minimum following distance analysis module, configured to: establish a nonlinear dynamic model of the following vehicle, and calculate a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model, and the prediction iteration of the vehicle's time-varying state data;

[0051] The optimal following strategy control module is used to: determine the optimal following distance interval based on the minimum following distance, analyze the influencing parameters between the following distance and the braking margin, energy consumption, lift coefficient, and the ability to track the leading vehicle; dynamically calculate the optimal following distance of the following vehicle within the optimal following distance interval based on the vehicle longitudinal dynamics model, the iterative relationship between the vehicle time-varying state data, and the influencing parameters; and control the following strategy of the unmanned vehicle fleet based on the optimal following distance.

[0052] On the other hand, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for optimizing the following vehicle distance calculation for unmanned vehicle platoon driving are implemented.

[0053] The beneficial effects of the technical solution of the present invention are:

[0054] 1. The method for optimizing the following distance between unmanned vehicles in a platoon, as described in the present invention, can obtain quantitative laws regarding the air drag coefficient and lift coefficient of the platoon vehicles, comprehensively consider vehicle speed, road resistance, transmission efficiency, drive / brake component response characteristics, and road slope angle, establish a nonlinear dynamic model, and dynamically predict and iteratively calculate the optimal following distance for each following vehicle in real time. Ultimately, while ensuring safety, it can effectively improve the driving efficiency and fuel economy of the unmanned vehicle platoon. The method features a comprehensive computational framework, strong adaptive adjustment capabilities, an accurate nonlinear dynamic model, and comprehensive consideration of multiple factors in the optimal distance calculation, along with strong scalability. It overcomes the shortcomings of existing technologies and has high application value.

[0055] 2. The unmanned vehicle platooning following vehicle spacing optimization calculation system described in the present invention can realize the unmanned vehicle platooning following vehicle spacing optimization calculation method described in the present invention through the mutual cooperation of system modules.

[0056] 3. The computer-readable storage medium described in the present invention can cooperate with the guidance system module to realize the unmanned vehicle formation travel following vehicle distance optimization calculation method described in the present invention, and the computer-readable storage medium described in the present invention also effectively improves the operability of the unmanned vehicle formation travel following vehicle distance optimization calculation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 1 is a flow chart of the method for optimizing the following vehicle spacing during platooning of unmanned vehicles according to Example 1 of the present invention;

[0059] Figure 2 Schematic diagram of the coordinate system used in the method for optimizing the following vehicle spacing in an unmanned vehicle platoon as described in Example 1 of the present invention;

[0060] Figure 31 is a schematic diagram of a calculation flow of the method for optimizing the following vehicle spacing for unmanned vehicle platooning according to Example 1 of the present invention;

[0061] Figure 4 1 is a schematic diagram of a curve showing the variation of the drag coefficient and the lift coefficient with the vehicle distance in the method for optimizing the following vehicle distance in the unmanned vehicle platooning according to Example 1 of the present invention;

[0062] Figure 5 1. This is a schematic diagram of the spatial relationship of the minimum following distance working condition in the method for optimizing the following distance calculation for unmanned vehicle platooning according to Example 1 of the present invention;

[0063] Figure 6 1. This is a schematic diagram of the time relationship corresponding to the spatial relationship of the minimum following distance working condition in the method for optimizing the following distance calculation for unmanned vehicle platooning according to Example 1 of the present invention;

[0064] Figure 7 2 is a schematic diagram of the architecture of the unmanned vehicle platooning following vehicle distance optimization calculation system described in Example 2 of the present invention. DETAILED DESCRIPTION

[0065] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more precise definition of the protection scope of the present invention.

[0066] In the description of the present invention, it should be noted that the embodiments described in the present invention are only part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of the present invention.

[0067] The terms "first," "second," and the like in the specification and claims herein and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in orders other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device.

[0068] In the description of the present invention, it should be noted that the following vehicle discussed in the present invention is a vehicle located in a non-leading position in a convoy, and the present invention studies the optimization problem of the desired following distance between the following vehicle and the vehicle in front, that is, the optimization problem of Δd; behind the following vehicle of the present invention, there may be other subsequent vehicles following the vehicle; the leading vehicle is not necessarily the first vehicle in the convoy, that is, the leading vehicle is not necessarily the leading vehicle.

[0069] In the description of the present invention, it should be noted that before the method of the present invention begins, a set of calculation coordinate systems O-xyz is established, such as Figure 2 The x-axis, pointing horizontally forward, represents the displacement ahead of the vehicle. The z-axis, pointing vertically upward, represents the vertical height. The y-axis is determined according to the right-hand rule. O-xyz is fixed to the front of the vehicle. The xy plane remains horizontal, with x always pointing toward the front of the vehicle. The vehicle speed v is actually the linear velocity of the vehicle, a scalar quantity. The angle between v and the x-axis is the slope angle α, which varies with the road topography.

[0070] Example 1: This example provides a method for optimizing the distance between vehicles in a platoon. Figures 1 to 6 As shown, the following steps are included:

[0071] S100, measuring the quantitative change characteristics of the air drag coefficient and lift coefficient of each vehicle in the unmanned vehicle fleet, including:

[0072] In a preferred embodiment, the air resistance coefficient C is fitted by wind tunnel test / CAE simulation. D and lift coefficient C L The wind resistance law function is as follows:

[0073] ;

[0074] In the above formula, i is the serial number of the following vehicle in the convoy, N is the number of vehicles in the convoy, Δd is the following distance, and v is the current speed of the following vehicle. Represents the corresponding functional relationship;

[0075] It should be noted that in the process of following a vehicle, factors such as the vehicle's shape, following speed, following distance, number of vehicles in the convoy, and vehicle serial number can all affect the vehicle's drag coefficient C. D and lift coefficient C L Show different patterns; such as Figure 4 Example (where C D∞is the drag coefficient when the following distance is infinite, that is, the vehicle is traveling alone). Analyzing this example, we can find that when the following distance is small, the wind resistance is smaller. When the distance increases to a certain extent, the change in the drag coefficient becomes smaller and smaller. For the fifth vehicle, its drag coefficient increases as the following distance decreases, which is counterintuitive. Furthermore, the lift coefficient increases as the distance decreases, which is also not conducive to the stable driving of the vehicle. In summary, the drag coefficient of the vehicle in the following process is C D and lift coefficient C L There are complex, nonlinear and even counterintuitive rules. Therefore, in order to calculate the optimal following distance, it is necessary to perform targeted calculations on different following conditions for different types of vehicles.

[0076] S200: Establish a nonlinear dynamic model of the following vehicle based on vehicle speed, road resistance, drive / brake component response characteristics, (particularly) road slope changes, changes in air resistance and lift caused by the platooning, and calculate the minimum following distance between the following vehicles based on this nonlinear dynamic model and the iterative prediction of various parameters, including:

[0077] S201: Determine the time iteration relationship, as follows:

[0078] (1) Draw a spatial relationship diagram of the minimum following distance working condition, such as Figure 5 As shown in the figure; for ease of description, the pilot vehicle and the following vehicles are drawn in two rows, and for ease of understanding, the road surface is drawn as a flat state; in the figure, the solid line vehicles are the relative x-axis positions of the pilot vehicle and the following vehicle just before the pilot vehicle starts braking; the dashed line vehicles are the relative positions of the pilot vehicle and the following vehicle when the vehicle stops braking; Δd is the following distance, and in the subsequent iterative calculation process, the variable D is used to represent the set initial following distance; d2 is the distance traveled by the pilot vehicle from the start of braking to the complete stop; d3 is the distance traveled by the following vehicle from the start of braking to the complete stop; because the pilot vehicle may be manually driven and its control law is uncertain, the pilot vehicle and the following vehicle do not necessarily stop at the same time, so there are three following distance relationships:

[0079] (1.1) , the two vehicles collided;

[0080] (1.2) , the two vehicles just do not collide, or it is called the critical collision point; under this extreme working condition, the leading vehicle usually stops at its maximum braking capacity, and the following vehicle also stops at its maximum braking capacity, and the following vehicle just does not collide with the leading vehicle. The minimum following distance is the working condition that focuses on following vehicle safety.

[0081] (1.3) , the two cars will not collide; in addition, Δd is also related to the slope. When the slope fluctuates, Δd should be the integral of the infinitesimal distance along the slope.

[0082] (2) Draw a time axis relationship diagram corresponding to the spatial relationship diagram of the minimum following distance working condition, such as Figure 6 ; Among them, for ease of understanding, the time axes of the pilot vehicle and the following vehicle are drawn separately; in the figure, t0 is the moment when the pilot vehicle starts braking, t2 is the moment when the pilot vehicle stops, t1 is the moment when the following vehicle has determined that the pilot vehicle has started braking and starts braking itself, and t3 is the moment when the following vehicle stops; the time interval between t0 and t1 may be affected by perception delay and logical calculation time, and is determined by pre-calibration; of course, with the support of advanced perception systems, the time interval between t0 and t1 may be very small or even negligible, that is, t0≈t1; since the order in which the pilot vehicle and the following vehicle stop is not constant, t2 and t3 will change according to the actual situation, and the following time conversion relationship exists:

[0083] ;

[0084] In order to facilitate subsequent iterative recursive prediction, K is used to represent the k moment. represents the corresponding time step; based on this, under the minimum following distance condition, the control strategy / calculation rule of the leading vehicle and the following vehicle is: the leading vehicle brakes at maximum intensity until it stops when k=0; the following vehicle maintains the motion state at k=0 within the interval k∈[0,K1], and brakes at maximum intensity until it stops within the interval k∈[K1,K3].

[0085] It should be noted that since the front vehicle may be driven by humans, its control strategy is uncertain, so uniform deceleration is assumed as the most reasonable prediction.

[0086] S202: Determine the time-varying speed v of the pilot vehicle when fully braking. p (k) and the iterative calculation relationship of the time-varying driving distance d2(k) are as follows:

[0087] (1) When the pilot vehicle brakes suddenly and stops at an ideal constant maximum deceleration, the relationship between the speed of the pilot vehicle and time from time t0 is:

[0088] ;

[0089] In this formula, v p The current speed of the pilot vehicle;

[0090] (2) When k=K2, the speed of the pilot vehicle decreases to 0. The conversion at this time is:

[0091] ;

[0092] In this formula, a pmax is the maximum braking deceleration of the pilot vehicle, which can be a constant value or a time-varying value;

[0093] (3) Therefore, the distance the pilot vehicle travels forward is The calculation relationship of change over time is:

[0094] .

[0095] S203: Determine an iterative calculation formula for the time-varying driving speed v(k) and the time-varying driving distance d3(k) of the following vehicle, as follows:

[0096] S2031. Construct the discrete state space equation of the system for the following vehicle, as follows:

[0097] (1) The following vehicle is set to maintain the motion state at time k = 0 in the interval k∈[0,K1], and brake with maximum intensity until it stops in the interval k∈[K1,K3];

[0098] (2) In the prior art, the maximum braking deceleration is often used to represent the maximum braking intensity of the following vehicle, which does not conform to the nonlinear characteristics of the actual vehicle. Therefore, in this step, a nonlinear MPC model predictive controller for the following vehicle motion is constructed to iteratively update the state of the following vehicle and transform the longitudinal dynamics model of the following vehicle to obtain the continuous state space equation based on the longitudinal dynamics model of the vehicle:

[0099] ;

[0100] In the above continuous state space equation, m is the vehicle mass, g is the acceleration of gravity, A is the vehicle's frontal area, ρ is the air density, and δ is the vehicle's rotational mass conversion coefficient. All of the above are known parameters. is the first-order derivative of the vehicle speed v, v is the state variable; f is the rolling resistance coefficient, C D is the air resistance coefficient, C L is the lift coefficient, α is the slope angle, all of which are dynamic parameters and non-constant values; F t The vehicle's traction / braking force is generated by the drive motor and mechanical brake elements acting as actuating elements through the interaction between the tires and the ground, and is calculated based on the response characteristics of the vehicle's components.

[0101] (3) Using forward Euler integration or other mature discretization methods (such as Runge-Kutta method and backward Euler integration, etc.), the above continuous state space equation is discretized to obtain the discrete state space equation of the system, as follows:

[0102] ;

[0103] In the discrete state-space equations of the above system, all time-varying variables are expressed as discrete values at time k. Based on the vehicle state at time k and the time-varying parameter values of the vehicle state in the discrete state-space equations of the above system, the state of the following vehicle at time k+1 can be obtained, such as v(k+1) and d(k+1).

[0104] S2032. Iterate the vehicle state time-varying parameters that affect the time-varying state of the following vehicle based on the system discrete state space equation of the following vehicle, as follows:

[0105] (1) Time-varying 、 C D and C L Iteration is performed, and the iterative relationship over time is:

[0106] ;

[0107] (2) Iterate the time-varying f(k), where f(k) is the road resistance coefficient in front of the vehicle, i.e., the driving resistance. This f(k) is related to the vehicle's speed. When factors such as the vehicle's tire tread type and total mass are determined, the following iterative relationship exists:

[0108] ;

[0109] (3) Iterate the time-varying α, where α is the manifold data of the road ramp in front of the vehicle, represented by a slope function, and is a time-varying function of x. α is calculated by iterative table lookup based on x(k);

[0110] (4) Traction / braking force F t (k) Iterate as follows:

[0111] (4.1) In the interval k∈[0,K1], keeping the current state unchanged, the iterative relationship between the system braking pressure and the output torque of the driving element over time is:

[0112] ;

[0113] In this relationship, P currentTo follow the vehicle's current mechanical brake system brake pressure, T current To follow the output torque of the vehicle's current drive element, T current A positive value is the driving torque. T current A negative value indicates braking torque. If the vehicle has multiple drive elements, the corresponding value here can be replaced with the state of multiple drive elements.

[0114] (4.2) In the interval k∈[K1,K3], all actuators output according to the maximum braking value. The iterative relationship between the system braking pressure and the output torque of the driving element over time is:

[0115] ;

[0116] In this relationship, the system braking pressure and output torque are not only limited by the slope, but also by its upper bound. The traction force is determined by these two values and is related to the road adhesion coefficient. Therefore, the mechanical braking pressure is also converted into the braking force at the tire, and finally it is obtained The iteration relationship is as follows:

[0117] ;

[0118] In the above formula, r is the vehicle radius; The mapping relationship between the mechanical brake pressure and the braking force on the wheel generated by the mechanical brake is calibrated in static tests based on the specific vehicle model; The efficiency characteristics of the drive system, usually a map; is the road adhesion coefficient.

[0119] S2033, based on S2031~S2032, iterate and recursively obtain the sequence of v(k) and d3(k) until v(k) When 0, it means that the following vehicle stops and exits the iteration, and the K3 and K2 corresponding to a certain D value can be obtained. .

[0120] S205: Set a certain following distance D, and based on the iterative relationship between d3(k), d2(k) and D, iterate D in a loop to calculate the minimum following distance d. min , as follows:

[0121] Set the following distance D. Based on the iterative relationship between d3(k), d2(k), and D, calculate the distances d2(K2) and d3(K3) traveled by the leading vehicle and the following vehicle when both vehicles brake to a stop. The iterative relationship is as follows:

[0122] (1) D d3(K3) When d2(K2) is reached, it is determined that the following vehicle and the lead vehicle have collided, and the following distance D is too small. It is necessary to increase the following distance D. d2(K2) and d3(K3) are calculated again based on the iterative relationship between d3(k), d2(k) and D, and compared.

[0123] (2) D = d3(K3) At d2(K2), it is judged that the following vehicle and the leading vehicle just do not collide, which is regarded as the critical collision point situation;

[0124] (3) D d3(K3) When d2(K2) is reached, it is determined that the following vehicle and the leading vehicle will not collide. It is determined that there is still sufficient following distance between the following vehicle and the leading vehicle. The following distance D can be reduced. Based on the iterative relationship between d3(k), d2(k) and D, d2(K2) and d3(K3) are calculated again and compared.

[0125] (4) In practical applications, D=d3(K3) is generally not used or appears. d2(K2), so set ε, ε is a small positive number (i.e., the first threshold), When the D value is obtained, the minimum following distance d min .

[0126] S300: Determine an optimal following distance interval. Based on all the aforementioned iterative relationships and the vehicle longitudinal dynamics model, and taking into account braking margin, energy consumption, lift coefficient, and tracking capability of the preceding vehicle, dynamically calculate the optimal following distance for any following vehicle within the optimal following distance interval. This optimal following distance is selected without colliding with the preceding vehicle, including:

[0127] S301: Determine the optimal interval for the following distance, as follows:

[0128] When d current <d min When the following distance is too close, there is a greater risk of collision. When the following distance is too large, the following vehicle may not be able to stably sense / listen to the position information of the leading vehicle, resulting in an unstable formation. Therefore, the critical point of the distance that may lead to the above situation is taken as the maximum following distance, which is recorded as d. max , then the optimal following distance should be in the following distance optimization interval d min and d max Between, that is [ ]; where d currentis the actual following distance between the ego vehicle and the pilot vehicle at the current moment, which can be obtained by wireless transmission from the pilot vehicle through intelligent networking or by detection by sensors and other components of the ego vehicle. current <d min When the following situation occurs, the vehicle's formation motion controller will be automatically alerted to indicate that the following vehicle is too close.

[0129] S302: Confirm The influencing parameters of d and braking margin, energy consumption, lift coefficient, and the ability to track the leading vehicle are as follows:

[0130] (1) Braking margin: d min Although it can ensure braking safety, it is too extreme. Most fleet managers hope to leave a certain margin when braking. Therefore, according to d min set up d and the influencing parameters of the braking margin;

[0131] (2) Energy consumption: In the case of stable following, energy consumption is predicted based on the equilibrium state of the aforementioned vehicle longitudinal dynamics model; when the vehicle is at a certain following distance D, the front and rear vehicles are in stable equilibrium, and the F at this time is determined. t (D) calculation formula, and based on the η map and the calculation formula, reverse lookup table to obtain the corresponding efficiency value η(D) (i.e. energy consumption data) at this time, and determine according to η(D) The influencing parameters between d and energy consumption;

[0132] Among them, F t The calculation formula of (D) is as follows:

[0133] ;

[0134] In addition, since the drag coefficient is not linear, it is necessary to use the traversal iteration method to solve it. min ,d max ] interval to find the minimum value of η(D), and the corresponding following distance is recorded as D best (i.e. the optimal efficiency value and vehicle distance);

[0135] (3) Lift coefficient C L :When the lift coefficient is too high, it will affect the adhesion effect and the braking force. At this time, the vehicle passengers or drivers will have a light and unstable feeling, which is not conducive to driving. Therefore, C L It also serves as an optimization condition for the following vehicle distance;

[0136] (4) Stable tracking capability of the preceding vehicle: When the distance to the preceding vehicle is too far, the vehicle-mounted sensor may lose the preceding vehicle. Therefore, the preceding vehicle confidence level B is used as the optimization condition for the following vehicle distance.

[0137] S303, based on the influence parameter d min 、D best 、C L and B, determine the final optimal function for optimizing the following distance , as follows:

[0138] ;

[0139] In the above-mentioned final optimization function, the norm can be 1-norm or 2-norm; according to the final optimization function , in [d min ,d max ]Interval d is optimized, and finally the optimal following distance for each following vehicle is obtained d best ; According to the optimal following distance d best Control the following speed and other driving control parameters between the following vehicle and the pilot vehicle.

[0140] It should be noted that the above examples are only for explaining the present invention and are not intended to limit the scope of protection of the present invention.

[0141] Example 2: This example is based on the same inventive concept as the method for optimizing the distance between vehicles traveling in an unmanned vehicle formation as described in Example 1, and provides an optimization calculation system for the distance between vehicles traveling in an unmanned vehicle formation. Figure 7 As shown, including:

[0142] The variation characteristics analysis module is used to analyze the quantitative variation characteristics of the air resistance coefficient and lift coefficient of vehicles in the unmanned vehicle fleet;

[0143] a minimum following distance analysis module, configured to: establish a nonlinear dynamic model of the following vehicle, and calculate a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model, and the prediction iteration of the vehicle's time-varying state data;

[0144] The optimal following strategy control module is used to: determine the optimal following distance interval based on the minimum following distance, analyze the influencing parameters between the following distance and the braking margin, energy consumption, lift coefficient, and the ability to track the leading vehicle; dynamically calculate the optimal following distance of the following vehicle within the optimal following distance interval based on the vehicle longitudinal dynamics model, the iterative relationship between the vehicle time-varying state data, and the influencing parameters; and control the following strategy of the unmanned vehicle fleet based on the optimal following distance.

[0145] Embodiment 3: This embodiment provides a computer-readable storage medium, including:

[0146] The storage medium is used to store computer software instructions used to implement the unmanned vehicle platoon traveling following vehicle spacing optimization calculation method described in the above-mentioned embodiment 1, which includes a program for executing the above-mentioned unmanned vehicle platoon traveling following vehicle spacing optimization calculation method; specifically, the executable program can be built into the unmanned vehicle platoon traveling following vehicle spacing optimization calculation system described in embodiment 2, so that the unmanned vehicle platoon traveling following vehicle spacing optimization calculation system can implement the unmanned vehicle platoon traveling following vehicle spacing optimization calculation method described in embodiment 1 by executing the built-in executable program.

[0147] In addition, the computer-readable storage medium of this embodiment may adopt any combination of one or more computer-readable storage media, wherein the computer-readable storage medium includes electrical, optical, electromagnetic, infrared or semiconductor systems, devices or components, or any combination thereof.

[0148] Different from the existing technology, the present application adopts a method, system and medium for optimizing the following distance calculation of unmanned vehicle formation driving, which can obtain the quantitative laws of the air resistance coefficient and lift coefficient of the formation vehicles, comprehensively consider the vehicle speed, road resistance, transmission efficiency, drive / braking element response characteristics and road slope angle, establish a nonlinear dynamic model, and dynamically predict and iteratively calculate the optimal following distance of each following vehicle in real time; ultimately, while ensuring safety, it can effectively improve the driving efficiency and fuel economy of the unmanned vehicle formation, with a complete calculation framework, strong adaptive adjustment ability, accurate nonlinear dynamic model, and comprehensive consideration of multiple factors in the optimal distance calculation and strong scalability, which makes up for the shortcomings of the existing technology and has high application value.

[0149] It should be understood that in the various embodiments of this document, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0150] It should also be understood that in the embodiments herein, the term "and / or" merely describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" could represent: A alone, A and B simultaneously, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0151] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0152] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0153] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices, or units, or can be an electrical, mechanical, or other form of connection.

[0154] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments herein.

[0155] In addition, the functional units in the various embodiments herein may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0156] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this article is essentially or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this article. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0157] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the following distance between vehicles in an unmanned vehicle platoon, characterized in that: The following steps are involved: Analyze the quantitative variation characteristics of the air drag coefficient and lift coefficient of vehicles in an unmanned vehicle fleet; Establishing a nonlinear dynamic model of the following vehicle, and calculating a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model, and the prediction iteration of the vehicle time-varying state data; An optimization interval for following distance is determined based on the minimum following distance, and the influencing parameters between following distance and braking margin, energy consumption, lift coefficient, and tracking capability of the leading vehicle are analyzed; an optimal following distance for the following vehicle is dynamically calculated within the optimization interval based on the vehicle longitudinal dynamics model, the iterative relationship between the vehicle time-varying state data, and the influencing parameters; and the following strategy of the unmanned vehicle fleet is controlled based on the optimal following distance.

2. The method for optimizing the following distance between vehicles in an unmanned vehicle platoon according to claim 1, characterized in that: The step of establishing a nonlinear dynamic model of the following vehicle and calculating a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model, and the prediction iteration of the vehicle's time-varying state data includes: determining a time iteration relationship during the vehicle following process, and determining an iteration relationship of the time-varying state data of the pilot vehicle when the pilot vehicle fully brakes based on the time iteration relationship; Calculating a discrete state space equation of the system based on the nonlinear dynamic model, and determining an iterative relationship of time-varying state data of the following vehicle according to the quantitative change characteristics and the discrete state space equation of the system; A dynamic following distance and a minimum distance condition are set, the dynamic following distance is iterated based on the time iteration relationship, the iteration relationship of the time-varying state data of the lead vehicle, the system discrete state space equation, and the iteration relationship of the time-varying state data of the following vehicle, and whether the iterated dynamic following distance is used as the minimum following distance is determined according to the minimum distance condition.

3. The method for optimizing the following distance between vehicles in an unmanned vehicle platoon according to claim 2 is characterized by: The discrete state space equation of the computing system based on the nonlinear dynamic model includes: constructing a nonlinear MPC model predictive controller for the following vehicle motion, and transforming a vehicle longitudinal dynamics model of the following vehicle according to the nonlinear MPC model predictive controller to obtain a continuous state space equation based on the vehicle longitudinal dynamics model; The continuous state space equation is discretized by using forward Euler integration to obtain the discrete state space equation of the system.

4. The method for optimizing the following distance between vehicles in an unmanned vehicle platoon according to claim 2, characterized in that: The time-varying state data of the pilot vehicle include: the time-varying driving speed and the time-varying driving distance of the pilot vehicle when fully braking; The time-varying state data of the following vehicle includes: the time-varying following distance of the following vehicle, the time-varying air resistance coefficient, the time-varying lift coefficient, the time-varying road resistance coefficient, the time-varying road slope manifold data, and the time-varying traction or braking force.

5. The method for optimizing the following distance between vehicles in an unmanned vehicle platoon according to claim 4 is characterized in that: The minimum distance condition includes: a first condition, a second condition, a third condition and a fourth condition; The first condition is: D d3(K3) d2(K2); The second condition is: D=d3(K3) d2(K2); The third condition is: D d3(K3) d2(K2); The fourth condition is: ; in, is the dynamic following distance, is the time-varying following distance of the following vehicle, is the time-varying distance traveled by the pilot vehicle when fully braking, is the first threshold value for the following distance.

6. The method for optimizing the following distance between vehicles in an unmanned vehicle platoon according to claim 5 is characterized by: The determining, according to the minimum distance condition, whether the iterative dynamic following distance is used as the minimum following distance includes: In response to the dynamic following distance satisfying the first condition, determining that the following vehicle and the lead vehicle have collided, increasing the dynamic following distance, iteratively calculating the time-varying following distance of the following vehicle and the time-varying travel distance of the lead vehicle under full braking based on the increased dynamic following distance, and determining the minimum distance condition; In response to the dynamic following distance satisfying the second condition, determining that a critical collision point occurs between the following vehicle and the lead vehicle; In response to the dynamic following distance satisfying the third condition, determining that the following vehicle and the lead vehicle do not collide, reducing the dynamic following distance, iteratively calculating the time-varying following distance of the following vehicle and the time-varying travel distance of the lead vehicle under full braking based on the reduced dynamic following distance, and determining the minimum distance condition; In response to the dynamic following distance satisfying the fourth condition, it is determined that the dynamic following distance is the minimum following distance.

7. The method for optimizing the following distance between vehicles in an unmanned vehicle platoon according to claim 2, characterized in that: The dynamically calculating the optimal following distance of the following vehicle within the following distance optimization range based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influencing parameter includes: Determining a final optimal function for the following vehicle distance based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influencing parameters; The following distance is dynamically optimized within the following distance optimization interval based on the final optimization function to obtain the optimal following distance.

8. The method for optimizing the following distance between vehicles in an unmanned vehicle platoon according to claim 7, characterized in that: The influencing parameters include: braking margin influencing parameters, energy consumption influencing parameters, lift coefficient influencing parameters and tracking capability influencing parameters; The braking margin influencing parameter is: the minimum following distance; The energy consumption influencing parameter is: an optimal efficiency value following distance determined based on energy consumption data iteration based on the iterative relationship between the vehicle longitudinal dynamics model, the vehicle time-varying state data, and the dynamic following distance; The lift coefficient influencing parameter is: a lift coefficient that varies with the dynamic following distance; The tracking capability influencing parameter is: a leading vehicle confidence level that varies with the dynamic following distance; The final optimization function is: ,in is the braking margin influencing parameter, is the energy consumption influencing parameter, is the lift coefficient influencing parameter, is the tracking capability influencing parameter, is the dynamic following distance.

9. A system for optimizing the distance between vehicles in an unmanned vehicle platoon, characterized in that: include: The variation characteristics analysis module is used to analyze the quantitative variation characteristics of the air resistance coefficient and lift coefficient of vehicles in the unmanned vehicle fleet; a minimum following distance analysis module, configured to: establish a nonlinear dynamic model of the following vehicle, and calculate a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model, and the prediction iteration of the vehicle's time-varying state data; The optimal following strategy control module is used to: determine the optimal following distance interval based on the minimum following distance, analyze the influencing parameters between the following distance and the braking margin, energy consumption, lift coefficient, and the ability to track the leading vehicle; dynamically calculate the optimal following distance of the following vehicle within the optimal following distance interval based on the vehicle longitudinal dynamics model, the iterative relationship between the vehicle time-varying state data, and the influencing parameters; and control the following strategy of the unmanned vehicle fleet based on the optimal following distance.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for optimizing the following vehicle distance of unmanned vehicle platooning according to any one of claims 1 to 8.

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